Doctoral Thesis: Latent Variable Models for Understanding User Behavior in Software Applications

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Event Speaker: 

Ardavan Saeedi

Event Location: 

32-D463 (Star)

Event Date/Time: 

Tuesday, November 28, 2017 - 3:00pm

Abstract: 
 
Understanding user behavior in software applications is of significant interest to software developers and companies. By having a better understanding of the user needs and usage patterns, the developers can design a more efficient workflow, add new features, or even automate the user's workflow. In this thesis, I propose novel latent variable models to understand, predict and eventually automate the user interaction with a software application. I start by analyzing users' clicks using time series models; I introduce models and inference algorithms for time series segmentation which are scalable to large-scale user datasets. Next, using deep generative models (e.g. conditional variational autoencoder) and some related models, I introduce a framework for automating the user interaction with a software application. I focus on photo enhancement applications, but this framework can be applied to any domain where segmentation, prediction and personalization is valuable. Finally, by combining sequential Monte Carlo and variational inference, I propose a new inference scheme which has better convergence properties than other reasonable baselines. 
 
Thesis Supervisors: Prof. Josh Tenenbaum and Prof. Ryan Adams (Princeton University)